Data Based Traffic Capacity Estimation of Waterway Networks

Abstract

Urban waterways play a crucial role in transportation, recreation, and logistics, yet their traffic capacity remains understudied compared to, for example, road and pedestrian traffic. This paper presents a novel methodology for estimating the capacity of urban waterway networks using empirical data, with a case study focusing on Amsterdam. Existing theoretical capacity models require calibration with real-world observations, which are often lacking. This study uses empirical data to develop a macroscopic capacity estimation model based on intensity-density relationships. Due to the distinct characteristics of urban water traffic, such as reaction times, risk adversity, regulations, and the dominant use case, traditional fundamental diagram models are not trivial to apply. Instead, an intensity percentile-based approach is introduced to define capacity, complemented by an important capacity marker that determines whether the estimated capacity represents the likelihood of the capacity being the actual capacity. The methodology is calibrated using expert-identified bottlenecks and validated through expert validation. Results indicate that the developed methodology shows promise for the capacity estimation of urban canals from dynamic data. However, further refinements are needed in the input data, the calibration of the model, and improving the methodology to account for missing key influencing factors to the traffic characteristics. Still this research provides a foundation for empirical data-driven waterway traffic research, offering policymakers insights for optimizing urban water transport operations.